
import itertools
import pandas as pd
from statsmodels.tsa.statespace.sarimax import SARIMAX
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error
import os

os.getcwd()

#df = pd.read_csv('Zip_zhvi_uc_sfr_tier_0.33_0.67_sm_sa_mon.csv')
df = pd.read_csv('Zip_zhvi_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv')

df.head()


df.columns

Index(['RegionID', 'SizeRank', 'RegionName', 'RegionType', 'StateName',
       'State', 'City', 'Metro', 'CountyName', '2000-01-31',
       ...
       '2023-03-31', '2023-04-30', '2023-05-31', '2023-06-30', '2023-07-31',
       '2023-08-31', '2023-09-30', '2023-10-31', '2023-11-30', '2023-12-31'],
      dtype='object', length=297)


df.shape

(26354, 297)


df.iloc[:, 9:].transpose()


df.shape

(26354, 297)


cutoff = round(df.shape[0]*0.9)
print("cutoff:",cutoff)

cutoff: 23719


import itertools
import pandas as pd
from statsmodels.tsa.statespace.sarimax import SARIMAX
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error

def rmse(y_true, y_pred):
    return np.sqrt(mean_squared_error(y_true, y_pred))

# def format_df(df, zip_code):
#     df = df[df['RegionName'] == zip_code]
#     df = df.iloc[:, 9:].transpose()
#     df.index = pd.to_datetime(df.index, infer_datetime_format=True)
#     df = df.rename(columns={df.columns[0]: 'Zillow Home Value Index (ZHVI)'})
#     df.dropna(inplace=True)
#     df['financial_crisis_flag'] = 0
#     df.loc['2007-08-30': '2012-01-31', 'financial_crisis_flag'] = 1
#     return df

# def format_df(df, zip_code):
#     if zip_code not in df['RegionName'].values:
#         raise ValueError(f"Zip code {zip_code} not found in DataFrame.")

#     df = df[df['RegionName'] == zip_code]

#     if df.empty:
#         raise ValueError(f"No data available for zip code {zip_code}.")

#     df = df.iloc[:, 9:].transpose()
    
#     if df.empty:
#         raise ValueError(f"Transposed DataFrame is empty for zip code {zip_code}.")

#     df.index = pd.to_datetime(df.index, infer_datetime_format=True)
#     df = df.rename(columns={df.columns[0]: 'Zillow Home Value Index (ZHVI)'})
#     df.dropna(inplace=True)
#     df['financial_crisis_flag'] = 0
#     df.loc['2007-08-30': '2012-01-31', 'financial_crisis_flag'] = 1

#     return df

def format_df(df, zip_code):
    if zip_code not in df['RegionName'].values:
        raise ValueError(f"Zip code {zip_code} not found in DataFrame.")

    df = df[df['RegionName'] == zip_code]

    if df.empty:
        raise ValueError(f"No data available for zip code {zip_code}.")

    df = df.iloc[:, 9:].transpose()
    
    if df.empty:
        raise ValueError(f"Transposed DataFrame is empty for zip code {zip_code}.")

    df.index = pd.to_datetime(df.index, infer_datetime_format=True)
    df = df.rename(columns={df.columns[0]: 'Zillow Home Value Index (ZHVI)'})
    df.dropna(inplace=True)

    # Set financial_crisis_flag for original financial crisis period
    df['financial_crisis_flag'] = 0
    df.loc['2007-08-30': '2012-01-31', 'financial_crisis_flag'] = 1

    # Set financial_crisis_flag for COVID lockdown period (adjust dates as needed)
    covid_start, covid_end = '2020-03-01', '2021-06-30'
    df.loc[covid_start: covid_end, 'financial_crisis_flag'] = 1

    return df


def find_sarimax_parameters(df):
    cutoff = round(df.shape[0]*0.8)
    train = df['Zillow Home Value Index (ZHVI)'][:cutoff].dropna()
    exog = df.dropna()['financial_crisis_flag'][:cutoff]
    print("len(exog):",len(exog))

    p = q = range(0, 4)
    #d = range(0, 3)
    d=[2]
    pdq = list(itertools.product(p, d, q))
    seasonal_pdq = [(x[0], x[1], x[2], 12) for x in itertools.product([0,1], [0], [0,1])]

    results_dict = {}

    for o in pdq:
        for so in seasonal_pdq:
            try:
                model = SARIMAX(endog=train, exog=exog, order=o, seasonal_order=so, freq='M').fit()
                results_dict[(o, so)] = model.aic
            except:
                continue

    rdf = pd.DataFrame(results_dict.items(), columns=['Parameters', 'AIC'])
    return rdf.sort_values('AIC').head(20)

def model(df, order, seasonal_order, zip_code):
    try:    
        cutoff = round(df.shape[0]*0.8)
        print("cutoff:",cutoff)

        train = df['Zillow Home Value Index (ZHVI)'][:cutoff].dropna()
        print("len(train):",len(train))

        exog_train = df['financial_crisis_flag'][:cutoff]
        print("len(exog_train):",len(exog_train))

        test = df['Zillow Home Value Index (ZHVI)'][cutoff:]
        print("len(test):",len(test))

        exog_test = df['financial_crisis_flag'][cutoff:].values.reshape(-1, 1)
        print("len(exog_test):",len(exog_test))


        #Experimenting with different optimization methods (e.g., 'nm', 'bfgs', 'lbfgs', etc, and maxiter=500
        model = SARIMAX(endog=train, exog=exog_train, order=order, seasonal_order=seasonal_order, freq='M', method='lbfgs', maxiter=500).fit(disp=False)
        #model = SARIMAX(endog=train, exog=exog_train, order=order, seasonal_order=seasonal_order, freq='M').fit()


        #y_hat_train = model.predict(start=train.index[2], typ='levels')
        #y_hat_test = model.predict(start=test.index[0], end=test.index[-1], typ='levels', exog=exog_test)

        # Ensure that the index you're accessing is within the range
        if len(train.index) > 2 and len(test.index) > 0:
            y_hat_train = model.predict(start=train.index[2], typ='levels')
            y_hat_test = model.predict(start=test.index[0], end=test.index[-1], typ='levels', exog=exog_test)
        else:
            raise IndexError("Insufficient data points for prediction.")


        print(model.summary())
        print(f'Test RMSE: {rmse(test, y_hat_test)}')
        print(f'Parameters Used: {order, seasonal_order}')

        fig, ax = plt.subplots(figsize=(12,8))
        ax.plot(train, label='train')
        ax.plot(test, label='test')
        ax.plot(y_hat_train, label='train prediction')
        ax.plot(y_hat_test, label='test prediction')
        ax.set_ylabel('Zillow Home Value Index (ZHVI)', fontsize=12)
        ax.set_xlabel('Year', fontsize=12)
        ax.set_title(f"ZHVI Forecasting for Zip Code {zip_code}")  # Include ZIP code in title
        plt.legend()
        plt.show()

        test_rmse = rmse(test, y_hat_test)
        #forecast_steps = len(test)
        #exog_forecast = np.zeros(forecast_steps).reshape(-1, 1) 

        # Use len(test) for forecast steps
        forecast_steps = len(test)  
        exog_forecast_length = len(exog_test)

        exog_forecast = np.zeros(forecast_steps).reshape(-1, 1) 

        # If exog_test has observations, use them for the initial part of the forecast
        if exog_forecast_length > 0:
            exog_forecast[:exog_forecast_length] = exog_test


        print("len(exog_forecast) Forecast time period in months:",len(exog_forecast))

        #forecast = model.forecast(steps=forecast_steps, exog=exog_forecast)
        forecast = model.forecast(steps=forecast_steps, exog=exog_forecast)

        # Last observed value in the TRAIN set
        last_train_value = train.iloc[-1]
        forecast_return = (forecast[-1] - last_train_value) / last_train_value * 100   

        return test_rmse, forecast_return
    
    except (ValueError, IndexError) as e:
        # Handle the error appropriately
        print(f"Error processing ZIP Code {zip_code}: {e}. Skipping...")
        return None, None 

def select_parameters_with_lowest_order(best_params_df):
    # Adding a new column for the sum of orders
    best_params_df['order_sum'] = best_params_df['Parameters'].apply(lambda x: sum(x[0]) + sum(x[1]))

    # Sorting first by AIC, then by order sum
    sorted_params = best_params_df.sort_values(by=['AIC', 'order_sum'])

    # Selecting the parameters with the lowest sum of orders
    selected_params = sorted_params.iloc[0]['Parameters']
    return selected_params

# def aggregate_results(zip_codes, df):
#     results = []
#     for zip_code in zip_codes:
#         if zip_code not in df['RegionName'].values:
#             print(f"Zip code {zip_code} not found in DataFrame. Skipping...")
#             continue

#         formatted_df = format_df(df, zip_code)
#         best_params_df = find_sarimax_parameters(formatted_df)

#         # Check if best_params_df is not empty before proceeding
#         if best_params_df.empty:
#             print(f"No suitable model parameters found for ZIP Code {zip_code}. Skipping...")
#             continue

#         best_order, best_seasonal_order = select_parameters_with_lowest_order(best_params_df)
#         test_rmse, forecast_return = model(formatted_df, best_order, best_seasonal_order, zip_code)
#         results.append({'Zip_Code': zip_code, 'Test_RMSE': test_rmse, 'Forecast_%_Appreciation': forecast_return, 'Parameters Used': best_order + best_seasonal_order})

#     return pd.DataFrame(results)

def aggregate_results(zip_codes, df):
    results = []
    for zip_code in zip_codes:
        try:
            if zip_code not in df['RegionName'].values:
                print(f"Zip code {zip_code} not found in DataFrame. Skipping...")
                continue

            formatted_df = format_df(df, zip_code)
            best_params_df = find_sarimax_parameters(formatted_df)

            # Check if best_params_df is not empty before proceeding
            if best_params_df.empty:
                print(f"No suitable model parameters found for ZIP Code {zip_code}. Skipping...")
                continue

            best_order, best_seasonal_order = select_parameters_with_lowest_order(best_params_df)
            test_rmse, forecast_return = model(formatted_df, best_order, best_seasonal_order, zip_code)
            results.append({'Zip_Code': zip_code, 'Test_RMSE': test_rmse, 'Forecast_%_Appreciation': forecast_return, 'Parameters Used': best_order + best_seasonal_order})

        except Exception as e:  # Catching any kind of exception
            print(f"Error processing ZIP Code {zip_code}: {e}. Skipping...")
            continue

    return pd.DataFrame(results)


## Updated version above!


# #zip_codes = [23120, 23112, 23224, 23832, 23238]
# zip_codes = [23103, 23106]
# results_df = aggregate_results(zip_codes, df)
# print(results_df)


# print(results_df)


# df[df['State'] == 'VA']['RegionName']


# df['RegionName'][:5]


# zip_codes = df[df['State'] == 'VA']['RegionName'][:5]
# results_df = aggregate_results(zip_codes, df)


# print(results_df)


# results_df


import geopandas as gpd
import matplotlib.pyplot as plt

# Step 1: Read the shapefile
shapefile_path_bkg = 'C:\\Users\\rsingh\\OneDrive - PlanRVA\\Documents\\DataHub-Rishabh\\Default Shapefiles\\va_zipcodes\\va-Zipcodes_RTC_PlanRVA.shp'  # Update with the path to your shapefile
#Planrva_file_Above
shapefile_path = 'C:\\Users\\rsingh\\OneDrive - PlanRVA\\Documents\\DataHub-Rishabh\\Default Shapefiles\\va_zipcodes\\tl_2019_us_zcta510\\tl_2019_us_zcta510.shp'
gdf = gpd.read_file(shapefile_path)
#When Planrva is the footprint 
gdf_bkg = gpd.read_file(shapefile_path_bkg)
gdf.head()


## PlanRVA region Zipcodes
zip_codes = gdf_bkg['GEOID10'].astype(int)


zip_codes

0     23089
1     23093
2     23102
3     23103
4     23106
      ...  
80    23038
81    23039
82    23047
83    23059
84    23060
Name: GEOID10, Length: 85, dtype: int32


# Rename 'old_column_name' to 'new_column_name'
gdf.rename(columns={'GEOID10': 'Zip_Code'}, inplace=True)


df.columns

Index(['RegionID', 'SizeRank', 'RegionName', 'RegionType', 'StateName',
       'State', 'City', 'Metro', 'CountyName', '2000-01-31',
       ...
       '2023-03-31', '2023-04-30', '2023-05-31', '2023-06-30', '2023-07-31',
       '2023-08-31', '2023-09-30', '2023-10-31', '2023-11-30', '2023-12-31'],
      dtype='object', length=297)


## Run model on desired df
results_df = aggregate_results(zip_codes, df)

len(exog): 230

C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'

cutoff: 230
len(train): 230
len(exog_train): 230
len(test): 58
len(exog_test): 58
                                     SARIMAX Results                                      
==========================================================================================
Dep. Variable:     Zillow Home Value Index (ZHVI)   No. Observations:                  230
Model:                           SARIMAX(2, 2, 1)   Log Likelihood               -1765.779
Date:                            Sat, 27 Jan 2024   AIC                           3541.559
Time:                                    01:35:47   BIC                           3558.706
Sample:                                01-31-2000   HQIC                          3548.477
                                     - 02-28-2019                                         
Covariance Type:                              opg                                         
=========================================================================================
                            coef    std err          z      P>|z|      [0.025      0.975]
-----------------------------------------------------------------------------------------
financial_crisis_flag    65.2649    511.953      0.127      0.899    -938.145    1068.675
ar.L1                     0.5859      0.314      1.868      0.062      -0.029       1.201
ar.L2                    -0.0649      0.019     -3.421      0.001      -0.102      -0.028
ma.L1                    -0.5617      0.314     -1.791      0.073      -1.177       0.053
sigma2                 3.009e+05   1.15e+04     26.197      0.000    2.78e+05    3.23e+05
===================================================================================
Ljung-Box (L1) (Q):                  11.36   Jarque-Bera (JB):              1931.19
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.11   Skew:                            -1.33
Prob(H) (two-sided):                  0.00   Kurtosis:                        17.01
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
Test RMSE: 33254.056059205395
Parameters Used: ((2, 2, 1), (0, 0, 0, 12))

len(exog_forecast) Forecast time period in months: 58
len(exog): 144

C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)

cutoff: 144
len(train): 144
len(exog_train): 144
len(test): 36
len(exog_test): 36

C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)

                                     SARIMAX Results                                      
==========================================================================================
Dep. Variable:     Zillow Home Value Index (ZHVI)   No. Observations:                  144
Model:                           SARIMAX(2, 2, 3)   Log Likelihood               -1003.760
Date:                            Sat, 27 Jan 2024   AIC                           2021.520
Time:                                    01:36:05   BIC                           2042.210
Sample:                                01-31-2009   HQIC                          2029.928
                                     - 12-31-2020                                         
Covariance Type:                              opg                                         
=========================================================================================
                            coef    std err          z      P>|z|      [0.025      0.975]
-----------------------------------------------------------------------------------------
financial_crisis_flag    62.9444    179.088      0.351      0.725    -288.062     413.951
ar.L1                     0.9536      0.062     15.481      0.000       0.833       1.074
ar.L2                    -0.9146      0.059    -15.554      0.000      -1.030      -0.799
ma.L1                    -0.9254      0.083    -11.085      0.000      -1.089      -0.762
ma.L2                     0.9190      0.074     12.360      0.000       0.773       1.065
ma.L3                    -0.0055      0.056     -0.100      0.921      -0.114       0.103
sigma2                 8.435e+04   8538.976      9.879      0.000    6.76e+04    1.01e+05
===================================================================================
Ljung-Box (L1) (Q):                   4.55   Jarque-Bera (JB):                17.26
Prob(Q):                              0.03   Prob(JB):                         0.00
Heteroskedasticity (H):               2.59   Skew:                             0.52
Prob(H) (two-sided):                  0.00   Kurtosis:                         4.35
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
Test RMSE: 39470.57837042498
Parameters Used: ((2, 2, 3), (0, 0, 0, 12))

C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\rsingh\anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)


print(results_df)

    Zip_Code      Test_RMSE  Forecast_%_Appreciation         Parameters Used
0      23089   33254.056059                19.569822  (2, 2, 1, 0, 0, 0, 12)
1      23093   39470.578370                64.105328  (2, 2, 3, 0, 0, 0, 12)
2      23102   63255.216466                51.521151  (2, 2, 2, 0, 0, 0, 12)
3      23103            NaN                      NaN  (1, 2, 1, 0, 0, 0, 12)
4      23106   28930.367662                53.022332  (3, 2, 2, 0, 0, 0, 12)
..       ...            ...                      ...                     ...
67     23038  262077.738065              -100.000000  (2, 2, 3, 1, 0, 1, 12)
68     23039   86882.121459                54.077651  (2, 2, 2, 1, 0, 1, 12)
69     23047   31957.012745                15.929120  (2, 2, 2, 1, 0, 1, 12)
70     23059   31534.324778                18.690472  (0, 2, 0, 0, 0, 0, 12)
71     23060   33830.631004                22.661455  (0, 2, 0, 0, 0, 0, 12)

[72 rows x 4 columns]


results_df


# Step 2: Merge with results_df
# Ensure that the 'Zip_Code' column in both dataframes are of the same data type
gdf['Zip_Code'] = gdf['Zip_Code'].astype(int)# Modify the column name if different in your shapefile
results_df['Zip_Code'] = results_df['Zip_Code'].astype(int)
merged_gdf = gdf.merge(results_df, on='Zip_Code', how='right')


print(results_df.describe())

           Zip_Code      Test_RMSE  Forecast_%_Appreciation
count     72.000000      66.000000                66.000000
mean   23295.638889   36415.547918                33.795687
std      305.920974   33876.146731                23.895277
min    23005.000000    5529.342245              -100.000000
25%    23099.750000   21335.605330                22.899958
50%    23183.000000   30269.839483                34.480273
75%    23236.250000   39379.317944                45.771065
max    23894.000000  262077.738065                80.817558


print(results_df.info())

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 72 entries, 0 to 71
Data columns (total 4 columns):
 #   Column                   Non-Null Count  Dtype  
---  ------                   --------------  -----  
 0   Zip_Code                 72 non-null     int32  
 1   Test_RMSE                66 non-null     float64
 2   Forecast_%_Appreciation  66 non-null     float64
 3   Parameters Used          72 non-null     object 
dtypes: float64(2), int32(1), object(1)
memory usage: 2.1+ KB
None


print(results_df.head())  # First 5 rows
print(results_df.tail())  # Last 5 rows

   Zip_Code     Test_RMSE  Forecast_%_Appreciation         Parameters Used
0     23089  33254.056059                19.569822  (2, 2, 1, 0, 0, 0, 12)
1     23093  39470.578370                64.105328  (2, 2, 3, 0, 0, 0, 12)
2     23102  63255.216466                51.521151  (2, 2, 2, 0, 0, 0, 12)
3     23103           NaN                      NaN  (1, 2, 1, 0, 0, 0, 12)
4     23106  28930.367662                53.022332  (3, 2, 2, 0, 0, 0, 12)
    Zip_Code      Test_RMSE  Forecast_%_Appreciation         Parameters Used
67     23038  262077.738065              -100.000000  (2, 2, 3, 1, 0, 1, 12)
68     23039   86882.121459                54.077651  (2, 2, 2, 1, 0, 1, 12)
69     23047   31957.012745                15.929120  (2, 2, 2, 1, 0, 1, 12)
70     23059   31534.324778                18.690472  (0, 2, 0, 0, 0, 0, 12)
71     23060   33830.631004                22.661455  (0, 2, 0, 0, 0, 0, 12)


sorted_df = results_df.sort_values(by='Forecast_%_Appreciation', ascending=False)
sorted_df


results_df.head()


merged_gdf.head()


# Step 3: Visualize the Map
fig, ax = plt.subplots(figsize=(10, 10))
# Plotting gdf_bkg as a white background
gdf_bkg.plot(ax=ax, color='white', edgecolor='black')
merged_gdf.plot(column='Test_RMSE', ax=ax, legend=True, cmap='viridis')
ax.set_title("Test RMSE by Zip Code")
plt.show()

C:\Users\rsingh\anaconda3\lib\site-packages\geopandas\plotting.py:48: ShapelyDeprecationWarning: The 'type' attribute is deprecated, and will be removed in the future. You can use the 'geom_type' attribute instead.
  if geom is not None and geom.type.startswith(prefix) and not geom.is_empty:
C:\Users\rsingh\anaconda3\lib\site-packages\geopandas\plotting.py:48: ShapelyDeprecationWarning: The 'type' attribute is deprecated, and will be removed in the future. You can use the 'geom_type' attribute instead.
  if geom is not None and geom.type.startswith(prefix) and not geom.is_empty:


# Step 3: Visualize the Map
fig, ax = plt.subplots(figsize=(10, 10))
# Plotting gdf_bkg as a white background
gdf_bkg.plot(ax=ax, color='white', edgecolor='black')
merged_gdf.plot(column='Forecast_%_Appreciation', ax=ax, legend=True, cmap='viridis')
ax.set_title("Forecast_%_Appreciation by ZipCode")
plt.show()

C:\Users\rsingh\anaconda3\lib\site-packages\geopandas\plotting.py:48: ShapelyDeprecationWarning: The 'type' attribute is deprecated, and will be removed in the future. You can use the 'geom_type' attribute instead.
  if geom is not None and geom.type.startswith(prefix) and not geom.is_empty:
C:\Users\rsingh\anaconda3\lib\site-packages\geopandas\plotting.py:48: ShapelyDeprecationWarning: The 'type' attribute is deprecated, and will be removed in the future. You can use the 'geom_type' attribute instead.
  if geom is not None and geom.type.startswith(prefix) and not geom.is_empty:


path1 = r"C:\Users\rsingh\OneDrive - PlanRVA\Documents\DataHub-Rishabh\Base locality Boundary\Loc_Boundary_RTC.shp"
gdf_counties = gpd.read_file(path1)


# Plotting
fig, ax = plt.subplots(figsize=(10, 10))
gdf_counties.plot(ax=ax, color='White', edgecolor='black', linewidth=0.5)

<AxesSubplot:>


import geopandas as gpd
import matplotlib.pyplot as plt

# Merge your geodataframe with your results_df
merged_gdf = gdf.merge(results_df, on='Zip_Code')

# Plotting
fig, ax = plt.subplots(figsize=(10, 10))

# Plotting the main data
merged_gdf.plot(column='Forecast_%_Appreciation', ax=ax, legend=True, cmap='viridis')

# Overlaying the county boundaries with transparent fill
gdf_counties.plot(ax=ax, color='none', edgecolor='black', linewidth=1)

# Setting plot limits to the extent of gdf_counties
minx, miny, maxx, maxy = gdf_counties.total_bounds
ax.set_xlim(minx, maxx)
ax.set_ylim(miny, maxy)

ax.set_title("Forecast_%_Appreciation by ZipCode")
plt.show()

C:\Users\rsingh\anaconda3\lib\site-packages\geopandas\plotting.py:48: ShapelyDeprecationWarning: The 'type' attribute is deprecated, and will be removed in the future. You can use the 'geom_type' attribute instead.
  if geom is not None and geom.type.startswith(prefix) and not geom.is_empty:


import geopandas as gpd
import matplotlib.pyplot as plt

# Merge your geodataframe with your results_df
merged_gdf = gdf.merge(results_df, on='Zip_Code')

# Plotting
fig, ax = plt.subplots(figsize=(10, 10))

# First, plot the county boundaries with white fill. This covers the entire plot area.
gdf_counties.plot(ax=ax, color='white', edgecolor='black', linewidth=0.5)

# Then plot the main data on top. Only areas within gdf_counties boundaries will be visible.
merged_gdf.plot(column='Forecast_%_Appreciation', ax=ax, legend=True, cmap='viridis')

# Setting plot limits to the extent of gdf_counties
minx, miny, maxx, maxy = gdf_counties.total_bounds
ax.set_xlim(minx, maxx)
ax.set_ylim(miny, maxy)

ax.set_title("Forecast_%_Appreciation by ZipCode")
plt.show()

C:\Users\rsingh\anaconda3\lib\site-packages\geopandas\plotting.py:48: ShapelyDeprecationWarning: The 'type' attribute is deprecated, and will be removed in the future. You can use the 'geom_type' attribute instead.
  if geom is not None and geom.type.startswith(prefix) and not geom.is_empty:


import geopandas as gpd
import matplotlib.pyplot as plt

# Merge your geodataframe with your results_df
merged_gdf = gdf.merge(results_df, on='Zip_Code')

# Clipping merged_gdf with gdf_counties
clipped_gdf = gpd.clip(merged_gdf, gdf_counties)

# Plotting
fig, ax = plt.subplots(figsize=(10, 10))

# Plot the county boundaries
gdf_counties.plot(ax=ax, color='none', edgecolor='black', linewidth=0.5)

# Plot the clipped data
clipped_gdf.plot(column='Forecast_%_Appreciation', ax=ax, legend=True, cmap='viridis')

# Setting plot limits to the extent of gdf_counties, if desired
minx, miny, maxx, maxy = gdf_counties.total_bounds
ax.set_xlim(minx, maxx)
ax.set_ylim(miny, maxy)

ax.set_title("Forecast_%_Appreciation by ZipCode")
plt.show()

C:\Users\rsingh\anaconda3\lib\site-packages\geopandas\plotting.py:48: ShapelyDeprecationWarning: The 'type' attribute is deprecated, and will be removed in the future. You can use the 'geom_type' attribute instead.
  if geom is not None and geom.type.startswith(prefix) and not geom.is_empty:
C:\Users\rsingh\anaconda3\lib\site-packages\geopandas\plotting.py:48: ShapelyDeprecationWarning: The 'type' attribute is deprecated, and will be removed in the future. You can use the 'geom_type' attribute instead.
  if geom is not None and geom.type.startswith(prefix) and not geom.is_empty:


import geopandas as gpd
import matplotlib.pyplot as plt

# Merge your geodataframe with your results_df
merged_gdf = gdf.merge(results_df, on='Zip_Code')

# Clipping merged_gdf with gdf_counties
clipped_gdf = gpd.clip(merged_gdf, gdf_counties)

# Plotting
fig, ax = plt.subplots(figsize=(10, 10))

# Plot the county boundaries
gdf_counties.plot(ax=ax, color='none', edgecolor='black', linewidth=0.5)

# Plot the clipped data
clipped_gdf.plot(column='Test_RMSE', ax=ax, legend=True, cmap='viridis')

# Setting plot limits to the extent of gdf_counties, if desired
minx, miny, maxx, maxy = gdf_counties.total_bounds
ax.set_xlim(minx, maxx)
ax.set_ylim(miny, maxy)

ax.set_title("Test RMSE by Zip Code")
plt.show()

C:\Users\rsingh\anaconda3\lib\site-packages\geopandas\plotting.py:48: ShapelyDeprecationWarning: The 'type' attribute is deprecated, and will be removed in the future. You can use the 'geom_type' attribute instead.
  if geom is not None and geom.type.startswith(prefix) and not geom.is_empty:
C:\Users\rsingh\anaconda3\lib\site-packages\geopandas\plotting.py:48: ShapelyDeprecationWarning: The 'type' attribute is deprecated, and will be removed in the future. You can use the 'geom_type' attribute instead.
  if geom is not None and geom.type.startswith(prefix) and not geom.is_empty:


## Next Steps: Forecast ahead - 5 Years, 10 Years ..


# Save the plot to a file
    if not os.path.exists(save_plot_dir):
        os.makedirs(save_plot_dir)
    plt.savefig(os.path.join(save_plot_dir, f'ZHVI_forecast_{zip_code}.png'))

    plt.show()


def model(df, order, seasonal_order):
    cutoff = round(df.shape[0]*0.9)
    train = df['Zillow Home Value Index (ZHVI)'][:cutoff].dropna()
    exog_train = df.dropna()['financial_crisis_flag'][:cutoff]
    test = df['Zillow Home Value Index (ZHVI)'][cutoff:]
    exog_test = np.zeros(len(test)).reshape(-1, 1)


    #Experimenting with different optimization methods (e.g., 'nm', 'bfgs', 'lbfgs', etc, and maxiter=500
    model = SARIMAX(endog=train, exog=exog_train, order=order, seasonal_order=seasonal_order, freq='M', method='lbfgs', maxiter=500).fit(disp=False)
    #model = SARIMAX(endog=train, exog=exog_train, order=order, seasonal_order=seasonal_order, freq='M').fit()
    y_hat_train = model.predict(start=train.index[2], typ='levels')
    y_hat_test = model.predict(start=test.index[0], end=test.index[-1], typ='levels', exog=exog_test)

    print(model.summary())
    print(f'Test RMSE: {rmse(test, y_hat_test)}')
    print(f'Parameters Used: {order, seasonal_order}')

    fig, ax = plt.subplots(figsize=(12,8))
    ax.plot(train, label='train')
    ax.plot(test, label='test')
    ax.plot(y_hat_train, label='train prediction')
    ax.plot(y_hat_test, label='test prediction')
    ax.set_ylabel('Zillow Home Value Index (ZHVI)', fontsize=12)
    ax.set_xlabel('Year', fontsize=12)
    plt.legend()
    plt.show()
    
    # Save the plot to a file
    save_plot_dir = r"C:\Users\rsingh\OneDrive - PlanRVA\Documents\Time_Series_GrowthProject\plots"
    if not os.path.exists(save_plot_dir):
        os.makedirs(save_plot_dir)
    plt.savefig(os.path.join(save_plot_dir, f'ZHVI_forecast_{zip_code}.png'))

    plt.show()
    
    test_rmse = rmse(test, y_hat_test)
    exog_forecast = np.zeros(12).reshape(-1, 1)
    forecast = model.forecast(steps=12, exog=exog_forecast)
    # Last observed value in the TRAIN set
    last_train_value = train.iloc[-1]
    forecast_return = (forecast[-1] - last_train_value) / last_train_value * 100

    return test_rmse, forecast_return

	RegionID	SizeRank	RegionName	RegionType	StateName	State	City	Metro	CountyName	2000-01-31	...	2023-03-31	2023-04-30	2023-05-31	2023-06-30	2023-07-31	2023-08-31	2023-09-30	2023-10-31	2023-11-30	2023-12-31
0	91982	1	77494	zip	TX	TX	Katy	Houston-The Woodlands-Sugar Land, TX	Fort Bend County	208752.177188	...	468400.012962	469600.790271	471587.869725	474687.471351	477460.027392	479919.299106	481726.908022	482958.479293	483644.390919	484254.484150
1	61148	2	8701	zip	NJ	NJ	Lakewood	New York-Newark-Jersey City, NY-NJ-PA	Ocean County	133799.965954	...	517054.419786	521100.063176	526320.736228	532105.809020	538056.023382	543798.549494	549960.546654	557112.585902	563739.635747	568385.787815
2	91940	3	77449	zip	TX	TX	Katy	Houston-The Woodlands-Sugar Land, TX	Harris County	102327.332899	...	274575.011824	273391.851083	272916.706411	273291.932508	274045.370378	274933.235990	275461.772882	275451.663963	275219.130604	274931.001539
3	62080	4	11368	zip	NY	NY	New York	New York-Newark-Jersey City, NY-NJ-PA	Queens County	147672.475058	...	480340.864596	473730.902787	466644.010241	461261.507623	459443.414913	459437.482269	458881.548977	456860.171677	453514.767036	449239.710048
4	91733	5	77084	zip	TX	TX	Houston	Houston-The Woodlands-Sugar Land, TX	Harris County	100957.722364	...	267553.019024	266769.398609	266628.259422	267219.988459	268054.762069	268918.974834	269342.669492	269333.119699	268917.017812	268499.368493

	0	1	2	3	4	5	6	7	8	9	...	26344	26345	26346	26347	26348	26349	26350	26351	26352	26353
2000-01-31	208752.177188	133799.965954	102327.332899	147672.475058	100957.722364	89209.529378	260775.195493	153242.924036	181060.959958	112567.262934	...	NaN	NaN	NaN	92897.827606	NaN	NaN	NaN	NaN	NaN	NaN
2000-02-29	208933.355223	134352.196216	102341.472343	149140.189415	100915.381721	89194.374796	261897.642882	153540.930524	181301.483813	113020.027694	...	NaN	NaN	NaN	93242.611189	NaN	NaN	NaN	NaN	NaN	NaN
2000-03-31	209417.484597	134725.444700	102215.866898	150242.618161	100736.106679	89213.721442	263318.510956	153690.642549	181605.552842	113694.729026	...	NaN	NaN	NaN	93555.552949	NaN	NaN	NaN	NaN	NaN	NaN
2000-04-30	210395.321387	135671.036962	102206.257761	152082.798879	100654.202432	89190.711160	265320.337636	154078.667948	182443.232876	114738.515131	...	NaN	NaN	NaN	93591.534454	NaN	NaN	NaN	NaN	NaN	NaN
2000-05-31	210430.298443	136654.797404	102252.914700	153402.530230	100629.838251	89215.579690	266758.899723	154412.141624	183719.323956	115661.091881	...	NaN	NaN	NaN	93761.792886	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2023-08-31	479919.299106	543798.549494	274933.235990	459437.482269	268918.974834	202834.973696	794531.597036	404537.125588	657428.859504	555302.427921	...	349103.150059	180495.338305	177392.795295	203073.135906	84201.214216	339422.454131	175266.666374	102138.167674	103008.548422	188893.327101
2023-09-30	481726.908022	549960.546654	275461.772882	458881.548977	269342.669492	203766.774891	792554.183994	402707.518398	656723.666185	562943.391380	...	350525.216453	179971.338387	176375.701615	204330.536466	84705.676269	341345.810653	175371.383285	102509.371773	104356.053262	189526.836516
2023-10-31	482958.479293	557112.585902	275451.663963	456860.171677	269333.119699	204465.130798	791285.829718	400968.770012	655585.521751	569213.775274	...	351066.696656	179101.409734	175829.886878	204894.639796	85641.204921	342917.881661	175565.910147	103003.798605	105233.060031	190129.583364
2023-11-30	483644.390919	563739.635747	275219.130604	453514.767036	268917.017812	204640.102855	790059.804507	399185.708326	655055.195335	574733.557641	...	351217.036317	177946.680047	175541.800674	204736.905723	86506.384636	343768.957866	175480.717039	102776.008170	105655.210102	190661.229579
2023-12-31	484254.484150	568385.787815	274931.001539	449239.710048	268499.368493	204820.250162	789922.404553	397292.509346	656671.669776	578625.465840	...	351324.667325	176831.704775	175871.874494	202979.091676	87050.209550	344129.353257	175335.009772	102900.456065	105737.932944	191578.956245

	ZCTA5CE10	GEOID10	CLASSFP10	MTFCC10	FUNCSTAT10	ALAND10	AWATER10	INTPTLAT10	INTPTLON10	geometry
0	43451	43451	B5	G6350	S	63484186	157689	+41.3183010	-083.6174935	POLYGON ((-83.70873 41.32733, -83.70815 41.327...
1	43452	43452	B5	G6350	S	121522304	13721730	+41.5157923	-082.9809454	POLYGON ((-83.08698 41.53780, -83.08256 41.537...
2	43456	43456	B5	G6350	S	9320975	1003775	+41.6318300	-082.8393923	MULTIPOLYGON (((-82.83558 41.71082, -82.83515 ...
3	43457	43457	B5	G6350	S	48004681	0	+41.2673301	-083.4274872	POLYGON ((-83.49650 41.25371, -83.48382 41.253...
4	43458	43458	B5	G6350	S	2573816	39915	+41.5304461	-083.2133648	POLYGON ((-83.22229 41.53102, -83.22228 41.532...

	ZCTA5CE10	Zip_Code	CLASSFP10	MTFCC10	FUNCSTAT10	ALAND10	AWATER10	INTPTLAT10	INTPTLON10	geometry	Test_RMSE	Forecast_%_Appreciation	Parameters Used
0	23089	23089	B5	G6350	S	156945786	16563560	+37.4557955	-076.9035007	POLYGON ((-76.98172 37.48980, -76.98163 37.490...	33254.056059	19.569822	(2, 2, 1, 0, 0, 0, 12)
1	23093	23093	B5	G6350	S	540280167	5234667	+38.0041674	-078.0400574	MULTIPOLYGON (((-78.12450 38.09174, -78.12438 ...	39470.578370	64.105328	(2, 2, 3, 0, 0, 0, 12)
2	23102	23102	B5	G6350	S	76839503	2667817	+37.7081193	-077.8286536	POLYGON ((-77.88998 37.66425, -77.88986 37.664...	63255.216466	51.521151	(2, 2, 2, 0, 0, 0, 12)
3	23103	23103	B5	G6350	S	96822168	3577185	+37.6480344	-077.7236401	POLYGON ((-77.82239 37.61005, -77.82196 37.610...	NaN	NaN	(1, 2, 1, 0, 0, 0, 12)
4	23106	23106	B5	G6350	S	69705539	1245598	+37.7161066	-077.2015109	POLYGON ((-77.27119 37.75150, -77.27111 37.751...	28930.367662	53.022332	(3, 2, 2, 0, 0, 0, 12)

	Zip_Code	Test_RMSE	Forecast_%_Appreciation	Parameters Used
24	23224	18024.145008	80.817558	(1, 2, 3, 0, 0, 0, 12)
62	23011	81679.444490	72.864093	(2, 2, 2, 0, 0, 0, 12)
65	23027	65095.620076	65.240710	(3, 2, 2, 0, 0, 1, 12)
1	23093	39470.578370	64.105328	(2, 2, 3, 0, 0, 0, 12)
66	23030	46743.155787	61.052270	(0, 2, 0, 0, 0, 0, 12)
...	...	...	...	...
7	23113	NaN	NaN	(2, 2, 2, 1, 0, 0, 12)
9	23117	NaN	NaN	(0, 2, 3, 0, 0, 0, 12)
12	23129	NaN	NaN	(0, 2, 0, 0, 0, 0, 12)
60	23894	NaN	NaN	(0, 2, 0, 0, 0, 0, 12)
63	23015	NaN	NaN	(0, 2, 0, 0, 0, 0, 12)